A checklist for documenting your attribution methodology so it survives an audit

A checklist for documenting your attribution methodology so it survives an audit

The question: six months from now, will anyone — including you — be able to reproduce how a channel earned its credit? Most attribution setups are undocumented folklore, and that's where disputes and quiet errors breed.

The documentation checklist:

— Record the model and its exact parameters: windows, half-lives, lookback, view-vs-click rules. 'We use data-driven' is not documentation.
— Log the data lineage: which touches are captured, which are missing, how identity is stitched, and the known size of the (direct) bucket.
— Write down every assumption: deduplication logic, how offline conversions enter, how you handle the cross-device gap.
— State the known biases explicitly — what the model can't see and which direction it skews.
— Version it. When you change a window, log the date; otherwise a credit shift looks like a market change.

The nuance: undocumented methodology isn't just a compliance risk — it's an analytical one. When the model changes silently, you misattribute the change to the market and act on a phantom. Reproducibility is the precondition for distinguishing a real shift from a config edit.

Bottom line for practitioners: document model, parameters, lineage, assumptions, and known biases in one versioned place. The most sophisticated attribution is worthless if no one can reconstruct why a number moved — and the discipline of writing down your biases is often where you discover them.